A quantum moving target segmentation algorithm for grayscale video
Wenjie Liu, Lu Wang, Qingshan Wu
TL;DR
This work tackles real-time moving target segmentation in grayscale video by introducing a quantum algorithm built on the QVNEQR video representation. The pipeline performs inter-frame processing in superposition, employing three-frame differences, a quantum comparator, binarization, and an AND fusion to extract moving targets, achieving a circuit complexity of $O(n^2+q)$. The approach offers exponential speedup over classical methods and demonstrates feasibility on IBM Q simulators in the NISQ era. Experimental results on a small quantum-video testbed align with classical segmentation outputs, validating both correctness and potential practicality.
Abstract
The moving target segmentation (MTS) aims to segment out moving targets in the video, however, the classical algorithm faces the huge challenge of real-time processing in the current video era. Some scholars have successfully demonstrated the quantum advantages in some video processing tasks, but not concerning moving target segmentation. In this paper, a quantum moving target segmentation algorithm for grayscale video is proposed, which can use quantum mechanism to simultaneously calculate the difference of all pixels in all adjacent frames and then quickly segment out the moving target. In addition, a feasible quantum comparator is designed to distinguish the grayscale values with the threshold. Then several quantum circuit units, including three-frame difference, binarization and AND operation, are designed in detail, and then are combined together to construct the complete quantum circuits for segmenting the moving target. For a quantum video with $2^m$ frames (every frame is a $2^n\times 2^n$ image with $q$ grayscale levels), the complexity of our algorithm can be reduced to O$(n^2 + q)$. Compared with the classic counterpart, it is an exponential speedup, while its complexity is also superior to the existing quantum algorithms. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.